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Artificial intelligence vs. Data Science: top 5 differences | by Rijul Singh Malik | Aug, 2022 – DataDrivenInvestor

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2 years ago

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A blog about the top 5 differences between AI and data science.

Photo by Possessed Photography on Unsplash

1. What is the difference between AI and Data science?

Artificial intelligence (AI) is an umbrella term that encompasses all efforts to create machines that can perform tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. Data science is the scientific approach to extracting knowledge from data in various forms, including structured and unstructured data, for example, text and images, in order to solve business problems. Data science is a relatively new term that refers to both the process and the people involved in analyzing data and developing new algorithms to extract insights from the data. Data science is a more general term, which subsumes a number of more focused disciplines, including machine learning, statistics, data mining and others.

The field of artificial intelligence (AI) is still in its infancy. There are many different types of AI, and each has its own sub-field of research. While some types of AI are more mature than others, AI is still evolving toward greater autonomy and more human-like intelligence. Data science is an umbrella term used to describe a number of disciplines, often used in the same context as artificial intelligence. Data science is the application of statistical analysis, machine learning, and other data-oriented concepts to solve a problem. It is not a single field, but rather a combination of fields. Data science, at its core, is about problem-solving and the construction of models for your data.

2. What is AI?

Artificial Intelligence is the general term for software performing tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence is a field of computer science that studies the theory behind intelligent behavior and, in particular, the ability to solve problems automatically. Artificial Intelligence is also referred to as AI and can be found in all forms of computers. One of the greatest applications of AI is in machine learning, which is a subset of AI. Machine learning and artificial intelligence are often used interchangeably, but they are different in that machine learning is a technique for programming a computer to learn how to do a task or make a decision on its own. Machine learning is related to but different from the broader field of Artificial intelligence. Artificial intelligence is a broad and loosely defined field that studies agents that perceive their environment and take actions that maximize their chances of success. This definition of artificial intelligence is very different from the one that is most often used in the mainstream media.

Artificial Intelligence (AI) is a booming technology in the world today. We see it in televisions, cars, and even our phones. But what is AI? Perhaps it’s better to ask what it is not. AI is not a science fiction movie villain that is out to destroy our world. AI is not a robot with a gun that is on a mission to take over. AI is not just a buzzword either. It’s much more than that. AI is a technology that is only as good as the data that feeds it.

3. What is Data Science?

Data science is a hot topic in the business world, but what exactly is it? Data science is a combination of statistics, computer science, and mathematics. Data scientists play a crucial role in a lot of business decisions, especially for big data and analytics. But what about artificial intelligence (AI)? Are the two terms interchangeable? What are the top 5 differences between AI and data science?

What is Data Science? Data science is the application of data mining, machine learning, artificial intelligence, statistics and other information-related disciplines in order to extract knowledge from data and turn it into useful information. Data science is not one specific field of study, but a set of skills that are used in many different disciplines. Data scientists are behind almost every big data success story. The data scientist of the future will be able to ask the right questions and develop the most important data-based products and services. Data science is evolving, but currently it is an exciting mix of statistics, machine learning, artificial intelligence, applied mathematics, programming, visualization, and communication.

Data science is a relatively new field that deals with the analysis and manipulation of large datasets. The main objective of data science is to make sense of a huge amount of data and to extract useful information from it. With the rise of the Internet, the number of data points available has increased exponentially. According to Forbes, a single human’s lifetime of social media data is equal to 5.2 billion books. As a result, data science has become a relevant field in today’s world, allowing businesses to collect and analyze vast amounts of information.

4. What do data science and AI have in common?

Artificial Intelligence (AI) and data science are two of the hottest technologies around today, but the two are often confused with one another. Data science and artificial intelligence are not the same thing. Data science is a collection of techniques for extracting knowledge from data, mainly for business and research purposes. Artificial intelligence is the ability for computers to learn to perform tasks that usually require human intelligence.

Artificial intelligence (AI) and data science are two popular fields, but what do they have in common? In reality, these terms have very little to do with each other and can be used interchangeably. That said, both fields are concerned with the way we use data to make better decisions. They both use various techniques to analyze sets of data to see if any correlations can be found between them. Data scientists use the findings from their analyses to decide which fields they want to explore further. This is where the two fields diverge. Artificial intelligence is the field of study dedicated to making computers do what they’re programmed to do — think. Data science is the field of study dedicated to making humans do what they’re programmed to do better.

5. How do AI and data science differ?

In the last few years, AI has been all the buzz in the media. And while it can seem like this technology has been around forever, in actuality, it’s only been around for a relatively short period of time. The first AI program was designed by Arthur Samuel in 1959, and the term AI was coined in the 1960s. And while the idea of AI has been around for decades, the technology behind it is still relatively new. A lot of people don’t know the difference between AI and data science, and even fewer know the top differences between the two. In this blog, I’m going to go over the top five differences.

In today’s world, artificial intelligence (AI) is all the buzz. But what is artificial intelligence? Does it have something to do with data science? Artificial intelligence is the study of making computer systems that mimic the way that humans think and learn, while data science is the application of statistical models, data sets, and statistical software to help solve problems and make predictions. Artificial intelligence is generally used to make predictions or to help computers learn, while data science is used to solve problems, help businesses, and make predictions.

Photo by Alexas_Fotos on Unsplash

Conclusion:

Is it AI or data science? Both are helping businesses to make better decisions.

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The Internet is Littered in ‘Educated Guesses’ Without the ‘Education’

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1 week ago

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October 8, 2024

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Although no one likes a know-it-all, they dominate the Internet.

The Internet began as a vast repository of information. It quickly became a breeding ground for self-proclaimed experts seeking what most people desire: recognition and money.

Today, anyone with an Internet connection and some typing skills can position themselves, regardless of their education or experience, as a subject matter expert (SME). From relationship advice, career coaching, and health and nutrition tips to citizen journalists practicing pseudo-journalism, the Internet is awash with individuals—Internet talking heads—sharing their “insights,” which are, in large part, essentially educated guesses without the education or experience.

The Internet has become a 24/7/365 sitcom where armchair experts think they’re the star.

Not long ago, years, sometimes decades, of dedicated work and acquiring education in one’s field was once required to be recognized as an expert. The knowledge and opinions of doctors, scientists, historians, et al. were respected due to their education and experience. Today, a social media account and a knack for hyperbole are all it takes to present oneself as an “expert” to achieve Internet fame that can be monetized.

On the Internet, nearly every piece of content is self-serving in some way.

The line between actual expertise and self-professed knowledge has become blurry as an out-of-focus selfie. Inadvertently, social media platforms have created an informal degree program where likes and shares are equivalent to degrees. After reading selective articles, they’ve found via and watching some TikTok videos, a person can post a video claiming they’re an herbal medicine expert. Their new “knowledge,” which their followers will absorb, claims that Panda dung tea—one of the most expensive teas in the world and isn’t what its name implies—cures everything from hypertension to existential crisis. Meanwhile, registered dietitians are shaking their heads, wondering how to compete against all the misinformation their clients are exposed to.

More disturbing are individuals obsessed with evangelizing their beliefs or conspiracy theories. These people write in-depth blog posts, such as Elvis Is Alive and the Moon Landings Were Staged, with links to obscure YouTube videos, websites, social media accounts, and blogs. Regardless of your beliefs, someone or a group on the Internet shares them, thus confirming your beliefs.

Misinformation is the Internet’s currency used to get likes, shares, and engagement; thus, it often spreads like a cosmic joke. Consider the prevalence of clickbait headlines:

  • You Won’t Believe What Taylor Swift Says About Climate Change!
  • This Bedtime Drink Melts Belly Fat While You Sleep!
  • In One Week, I Turned $10 Into $1 Million!

Titles that make outrageous claims are how the content creator gets reads and views, which generates revenue via affiliate marketing, product placement, and pay-per-click (PPC) ads. Clickbait headlines are how you end up watching a TikTok video by a purported nutrition expert adamantly asserting you can lose belly fat while you sleep by drinking, for 14 consecutive days, a concoction of raw eggs, cinnamon, and apple cider vinegar 15 minutes before going to bed.

Our constant search for answers that’ll explain our convoluted world and our desire for shortcuts to success is how Internet talking heads achieve influencer status. Because we tend to seek low-hanging fruits, we listen to those with little experience or knowledge of the topics they discuss yet are astute enough to know what most people want to hear.

There’s a trend, more disturbing than spreading misinformation, that needs to be called out: individuals who’ve never achieved significant wealth or traded stocks giving how-to-make-easy-money advice, the appeal of which is undeniable. Several people I know have lost substantial money by following the “advice” of Internet talking heads.

Anyone on social media claiming to have a foolproof money-making strategy is lying. They wouldn’t be peddling their money-making strategy if they could make easy money.

Successful people tend to be secretive.

Social media companies design their respective algorithms to serve their advertisers—their source of revenue—interest; hence, content from Internet talking heads appears most prominent in your feeds. When a video of a self-professed expert goes viral, likely because it pressed an emotional button, the more people see it, the more engagement it receives, such as likes, shares and comments, creating a cycle akin to a tornado.

Imagine scrolling through your TikTok feed and stumbling upon a “scientist” who claims they can predict the weather using only aluminum foil, copper wire, sea salt and baking soda. You chuckle, but you notice his video got over 7,000 likes, has been shared over 600 times and received over 400 comments. You think to yourself, “Maybe this guy is onto something.” What started as a quest to achieve Internet fame evolved into an Internet-wide belief that weather forecasting can be as easy as DIY crafts.

Since anyone can call themselves “an expert,” you must cultivate critical thinking skills to distinguish genuine expertise from self-professed experts’ self-promoting nonsense. While the absurdity of the Internet can be entertaining, misinformation has serious consequences. The next time you read a headline that sounds too good to be true, it’s probably an Internet talking head making an educated guess; without the education seeking Internet fame, they can monetize.

______________________________________________________________

 

Nick Kossovan, a self-described connoisseur of human psychology, writes about what’s

on his mind from Toronto. You can follow Nick on Twitter and Instagram @NKossovan.

 

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Tight deadlines on software projects can put safety at risk: survey

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October 8, 2024

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TORONTO – A new survey says a majority of software engineers and developers feel tight project deadlines can put safety at risk.

Seventy-five per cent of the 1,000 global workers who responded to the survey released Tuesday say pressure to deliver projects on time and on budget could be compromising critical aspects like safety.

The concern is even higher among engineers and developers in North America, with 77 per cent of those surveyed on the continent reporting the urgency of projects could be straining safety.

The study was conducted between July and September by research agency Coleman Parkes and commissioned by BlackBerry Ltd.’s QNX division, which builds connected-car technology.

The results reflect a timeless tug of war engineers and developers grapple with as they balance the need to meet project deadlines with regulations and safety checks that can slow down the process.

Finding that balance is an issue that developers of even the simplest appliances face because of advancements in technology, said John Wall, a senior vice-president at BlackBerry and head of QNX.

“The software is getting more complicated and there is more software whether it’s in a vehicle, robotics, a toaster, you name it… so being able to patch vulnerabilities, to prevent bad actors from doing malicious acts is becoming more and more important,” he said.

The medical, industrial and automotive industries have standardized safety measures and anything they produce undergoes rigorous testing, but that work doesn’t happen overnight. It has to be carried out from the start and then at every step of the development process.

“What makes safety and security difficult is it’s an ongoing thing,” Wall said. “It’s not something where you’ve done it, and you are finished.”

The Waterloo, Ont.-based business found 90 per cent of its survey respondents reported that organizations are prioritizing safety.

However, when asked about why safety may not be a priority for their organization, 46 per cent of those surveyed answered cost pressures and 35 per cent said a lack of resources.

That doesn’t surprise Wall. Delays have become rampant in the development of tech, and in some cases, stand to push back the launch of vehicle lines by two years, he said.

“We have to make sure that people don’t compromise on safety and security to be able to get products out quicker,” he said.

“What we don’t want to see is people cutting corners and creating unsafe situations.”

The survey also took a peek at security breaches, which have hit major companies like London Drugs, Indigo Books & Music, Giant Tiger and Ticketmaster in recent years.

About 40 per cent of the survey’s respondents said they have encountered a security breach in their employer’s operating system. Those breaches resulted in major impacts for 27 per cent of respondents, moderate impacts for 42 per cent and minor impacts for 27 per cent.

“There are vulnerabilities all the time and this is what makes the job very difficult because when you ship the software, presumably the software has no security vulnerabilities, but things get discovered after the fact,” Wall said.

Security issues, he added, have really come to the forefront of the problems developers face, so “really without security, you have no safety.”

This report by The Canadian Press was first published Oct. 8, 2024.

Companies in this story: (TSX:BB)

The Canadian Press. All rights reserved.

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Beware of scams during Amazon’s Prime Big Deal Days sales event: cybersecurity firm

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October 8, 2024

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As online shoppers hunt for bargains offered by Amazon during its annual fall sale this week, cybersecurity researchers are warning Canadians to beware of an influx of scammers posing as the tech giant.

In the 30 days leading up to Amazon’s Prime Big Deal Days, taking place Tuesday and Wednesday, there were more than 1,000 newly registered Amazon-related web domains, according to Check Point Software Technologies, a company that offers cybersecurity solutions.

The company said it deemed 88 per cent of those domains malicious or suspicious, suggesting they could have been set up by scammers to prey on vulnerable consumers. One in every 54 newly created Amazon-related domain included the phrase “Amazon Prime.”

“They’re almost indiscernible from the real Amazon domain,” said Robert Falzon, head of engineering at Check Point in Canada.

“With all these domains registered that look so similar, it’s tricking a lot of people. And that’s the whole intent here.”

Falzon said Check Point Research sees an uptick in attempted scams around big online shopping days throughout the year, including Prime Days.

Scams often come in the form of phishing emails, which are deceptive messages that appear to be from a reputable source in attempt to steal sensitive information.

In this case, he said scammers posing as Amazon commonly offer “outrageous” deals that appear to be associated with Prime Days, in order to trick recipients into clicking on a malicious link.

The cybersecurity firm said it has identified and blocked 100 unique Amazon Prime-themed scam emails targeting organizations and consumers over the past two weeks.

Scammers also target Prime members with unsolicited calls, claiming urgent account issues and requesting payment information.

“It’s like Christmas for them,” said Falzon.

“People expect there to be significant savings on Prime Day, so they’re not shocked that they see something of significant value. Usually, the old adage applies: If it seems too good to be true, it probably is.”

Amazon’s website lists a number of red flags that it recommends customers watch for to identify a potential impersonation scam.

Those include false urgency, requests for personal information, or indications that the sender prefers to complete the purchase outside of the Amazon website or mobile app.

Scammers may also request that customers exclusively pay with gift cards, a claim code or PIN. Any notifications about an order or delivery for an unexpected item should also raise alarm bells, the company says.

“During busy shopping moments, we tend to see a rise in impersonation scams reported by customers,” said Amazon spokeswoman Octavia Roufogalis in a statement.

“We will continue to invest in protecting consumers and educating the public on scam avoidance. We encourage consumers to report suspected scams to us so that we can protect their accounts and refer bad actors to law enforcement to help keep consumers safe.”

Falzon added that these scams are more successful than people might think.

As of June 30, the Canadian Anti-Fraud Centre said there had been $284 million lost to fraud so far this year, affecting 15,941 victims.

But Falzon said many incidents go unreported, as some Canadians who are targeted do not know how or where to flag a scam, or may choose not to out of embarrassment.

Check Point recommends Amazon customers take precautions while shopping on Prime Days, including by checking URLs carefully, creating strong passwords on their accounts, and avoiding personal information being shared such as their birthday or social security number.

The cybersecurity company said consumers should also look for “https” at the beginning of a website URL, which indicates a secure connection, and use credit cards rather than debit cards for online shopping, which offer better protection and less liability if stolen.

This report by The Canadian Press was first published Oct. 8, 2024.

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